As computers become more and more integrated into our modern society, there is a growing need for tools that will allow a user to efficiently enter data into a computer. Some of the most desired input tools are those that allow a user to enter data using natural input techniques rather than typing on a physical or software QWERTY keyboard. These natural input techniques, which include handwriting and speech, offer users the promise of a familiar and convenient method of creating data that requires no special skills. To this end, great strides have been made in developing software applications that recognize text from such natural input. For example, some voice recognition applications will accurately transcribe text from a user's speech. Similarly, some handwriting recognition applications will recognize text from both block print and cursive handwriting.
One important advantage to these natural input techniques is that they potentially offer users a much higher throughput than entering data through a keyboard. The typical user can speak considerably faster than he or she can type. Thus, a user can quickly input text by speaking words that would have otherwise taken much longer to input by typing on a keyboard. Likewise, if a user has not memorized the position of keys on a QWERTY keyboard, that user may be able to handwrite words with a stylus and a digitizer faster than he or she can type those words on a keyboard.
While natural input offers the potential for high throughput in theory, in actuality the inherent inaccuracies of natural input can significantly reduce that potential. For example, a user may dictate voice input that initially is translated into text at a rate of 120 words per minute. After the user proofreads and corrects any errors in the recognized text, however, the user's actual throughput may drop to only 80 words per minute or less. Typed text generally has a few typographical errors, but text recognized from speech or handwriting usually will contain many errors as a result of inaccurate recognition. To compound this drawback, conventional natural input correction methods for correcting these errors are time consuming.
A conventional natural input correction method typically requires a user to first move a pointer to select the text to be corrected. Next, the user must move the pointer to a command button at a different location, such as a button on a toolbar, in order to retrieve a menu listing alternate selections for the inaccurately recognized text. The user must then move the pointer to still a third location to select the correct alternate selection on the alternates list. Because the user must continuously move the pointer from the text to a command button and then to an alternate list, the user suffers a significant delay in correcting even a single inaccurately recognized word. Thus, the very process of correcting text recognized from natural input may substantially reduce the throughput of that natural input technique. Correspondingly, any reduction in the speed of the correction process will help increase the throughput for natural input and generally improve the user's experience for natural input. Accordingly, there is a need for a natural input correction tool that allows a user to quickly and efficiently correct text.